Late Fusion of Multiple Convolutional Layers for Pedestrian Detection - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year :

Late Fusion of Multiple Convolutional Layers for Pedestrian Detection

Abstract

We propose a system design for pedestrian detection by leveraging the power of multiple convolutional layers explicitly. We quantify the effect of different convolutional layers on the detection of pedestrians of varying scales and occlusion level. We show that earlier convolutional layers are better at handling small-scale and partially oc-cluded pedestrians. We take cue from these conclusions and propose a pedestrian detection system design based on Faster-RCNN which leverages multiple convolutional layers by late fusion. In our design, we introduce height-awareness in the loss function to make the network emphasize on pedestrian heights which are misclassified during the training process. The proposed system design achieves a log-average miss-rate of 9.25% on the caltech-reasonable dataset. This is within 1.5% of the current state-of-art approach , while being a more compact system.
Fichier principal
Vignette du fichier
Late_Fusion_of_Multiple_Convolutional_Layers_for_Pedestrian_Detection.pdf (234.62 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01926073 , version 1 (18-11-2018)

Identifiers

  • HAL Id : hal-01926073 , version 1

Cite

Ujjwal Ujjwal, Aziz Dziri, Bertrand Leroy, Francois Bremond. Late Fusion of Multiple Convolutional Layers for Pedestrian Detection. 15th IEEE International Conference on Advanced Video and Signal-based Surveillance, Nov 2018, Auckland, New Zealand. ⟨hal-01926073⟩
122 View
216 Download

Share

Gmail Facebook Twitter LinkedIn More